Feedback-based Approach Research Articles

A Feedback-Based Approach for Mainstream Traffic Flow Control of Multiple Bottlenecks on Motorways

Mainstream traffic flow control (MTFC) enabled via variable speed limits (VSLs) has been investigated in previous studies, utilizing various control strategies. In this paper a new feedback control strategy is proposed for MTFC enabled via VSLs, considering multiple bottleneck locations. Results are evaluated using a validated macroscopic model. The feedback concept is robust and can be immediately implemented in the field as it considers practical and safety constraints.

Fast servo signal acquisition in tape drives using servo and data channels

In a tape drive, the head assembly hosting servo readers and data writers/readers must initially be moved to a target position, so that information can be written to or read from the desired tracks. In tape systems with flangeless rollers, as recently introduced in IBM’s tape drives for improved performance and extended drive and tape lifetime, the initial positioning of the servo readers over the servo bands, from which the position-error signal for steady-state track-following operation is obtained, has to be fast. This requirement is dictated by the nonnegligible lateral tape motion experienced in tape paths with flangeless rollers. In this paper, a method is proposed to utilize both the servo readers and the data readers in a drive to detect the presence of a valid servo signal. If the servo readers are not positioned over a servo band, the distance between servo readers and servo band can thereby be continuously estimated by identifying the data reader detecting the presence of a servo signal. This information is then provided to a control element that controls the joint operation of a coarse stepper motor and a voice-coil motor based fine actuator with large stroke in a feedback or feed-forward configuration to achieve fast servo reader positioning. In the feedback-based approach, all data channels are continuously monitored to detect the presence of a valid servo signal and a pseudo position-error signal is generated and fed back to the control unit to drive the actuator towards the target position. In the feed-forward approach, a trajectory for the actuator is determined as soon as the servo band is in the capture range of the fine actuator and a control signal is generated so that the servo reader rapidly lands over the servo band with optimum control effort.

ON BIORTHOGONAL WAVELETS RELATED TO THE WALSH FUNCTIONS

In this paper, we describe an algorithm for computing biorthogonal compactly supported dyadic wavelets related to the Walsh functions on the positive half-line ℝ+. It is noted that a similar technique can be applied in very general situations, e.g., in the case of Cantor and Vilenkin groups. Using the feedback-based approach, some numerical experiments comparing orthogonal and biorthogonal dyadic wavelets with the Haar, Daubechies, and biorthogonal 9/7 wavelets are prepared.

Use of feedback strategies in the detection of events for video surveillance

The authors present a feedback-based approach for event detection in video surveillance that improves the detection accuracy and dynamically adapts the computational effort depending on the complexity of the analysed data. A core feedback structure is proposed based on defining different levels of detail for the analysis performed and estimating the complexity of the data being analysed. Then, three feedback-based analysis strategies are defined (based on this core structure) and introduced in the processing stages of a typical video surveillance system. A rule-based system is designed to manage the interaction between these feedback-strategies. Experimental results show that the proposed approach slightly increases the detection reliability, whereas highly reduces the computational effort as compared to the initially developed surveillance system (without feedback strategies) across a variety of multiple video surveillance scenarios operating at real time.

Feedback process scheduling with simple discrete-time control structures

This manuscript addresses the problem of process scheduling in a multitasking computing environment. The mainstream feedback-based approach to that problem preserves the existing scheduler, and adapts some of its parameters by means of convenient loops. On the contrary, in this research, the scheduler is entirely replaced by suitable control structures, synthesised and analysed in the discrete-time domain. The proposed approach allows for a clear interpretability of the involved parameters, whereas the complexity of the obtained scheduling solutions is comparable to the existing ones. Simulation examples support the above claims. The focus is here restricted for convenience to the preemptive single-processor case, although several generalisations are possible.

The Magnetic Resonator Piano: Electronic Augmentation of an Acoustic Grand Piano

This paper presents the magnetic resonator piano, a hybrid acoustic-electronic instrument extending the traditional grand piano. Sound is produced without loudspeakers using electromagnetic actuators to directly manipulate the piano strings, expanding its vocabulary to include infinite sustain, notes that crescendo from silence, harmonics, and a variety of timbres. A feedback-based approach senses string vibrations from a single pickup on the soundboard, using filters and phase-locked loops to generate signals which reinforce the natural motion of each string. Signal-routing hardware enables coverage of all 88 notes of the piano using a standard digital audio interface. This paper describes the hardware, software, and performing interface of the instrument and presents measurements of amplitude, frequency response, and phase-locked loop performance. Sounds generated by the actuators achieve similar amplitudes to the traditional piano, and while they cannot replicate the rapid attack produced by a piano hammer, they complement the percussive nature of the piano by enabling continuous legato control of amplitude, frequency, and timbre.

A Feedback-Based Approach to DVFS in Data-Flow Applications

Runtime frequency and voltage adaptation has become very attractive for current and next generation embedded multicore platforms because it allows handling the workload variabilities arising in complex and dynamic utilization scenarios. The main challenge of dynamic frequency adaptation is to adjust the processing speed of each element to match the quality-of-service requirements in the presence of workload variations. In this paper, we present a control theoretic approach to dynamic voltage/frequency scaling for data-flow models of computations mapped to multiprocessor systems-on-chip architectures. We discuss, in particular, nonlinear control approaches to deal with general streaming applications containing both pipeline and parallel stages. Theoretical analysis and experiments, carried out by means of a cycle-accurate energy-aware multiprocessor simulation platform, are provided. We have applied the proposed control approach to realistic streaming applications such as Data Encryption Standard and software-based FM radio.

Cooperative Transmission for Wireless Relay Networks Using Limited Feedback

To achieve the available performance gains in half-duplex wireless relay networks, several cooperative schemes have been earlier proposed using either distributed space-time coding or distributed beamforming for the transmitter without and with channel state information (CSI), respectively. However, these schemes typically have rather high implementation and/or decoding complexities, especially when the number of relays is high. In this paper, we propose a simple low-rate feedback-based approach to achieve maximum diversity with a low decoding and implementation complexity. To further improve the performance of the proposed scheme, the knowledge of the second-order channel statistics is exploited to design long-term power loading through maximizing the receiver signal-to-noise ratio (SNR) with appropriate constraints. This maximization problem is approximated by a convex feasibility problem whose solution is shown to be close to the optimal one in terms of the error probability. Subsequently, to provide robustness against feedback errors and further decrease the feedback rate, an extended version of the distributed Alamouti code is proposed. It is also shown that our scheme can be generalized to the differential transmission case, where it can be applied to wireless relay networks with no CSI available at the receiver.

System support for exploration and expert feedback in resolving conflicts during integration of metadata

A critical reality in integration is that knowledge obtained from different sources may often be conflicting. Conflict-resolution, whether performed during the design phase or during run-time, can be costly and, if done without a proper understanding of the usage context, can be ineffective. In this paper, we propose a novel exploration and feedback-based approach [FICSR (Pronounced as "fixer")] to conflict-resolution when integrating metadata from different sources. Rather than relying on purely automated conflict-resolution mechanisms, FICSR brings the domain expert in the conflict-resolution process and informs the integration based on the expert's feedback. In particular, instead of relying on traditional model based definition of consistency (which, whenever there are conflicts, picks a possible world among many), we introduce a ranked interpretation of the metadata and statements about the metadata. This not only enables FICSR to avoid committing to an interpretation too early, but also helps in achieving a more direct correspondence between the experts' (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for exploratory feedback for conflict-resolution: within the context of a given statement of interest, (a) a preliminary ranking of candidate matches, representing different resolutions of the conflicts, informs the user about the alternative interpretations of the metadata, while (b) user feedback regarding the preferences among alternatives is exploited to inform the system about the expert's relevant domain knowledge. The expert's feedback, then, is used for resolving not only the conflicts among different sources, but also possible mis-alignments due to the initial matching phase. To enable this $${(system \stackrel{_{informs}}{\longleftrightarrow} user)}$$ feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated metadata. We also develop algorithms for efficient on-line query processing, candidate result enumeration, validity analysis, and system feedback. The results are brought together and evaluated in the Feedback-based InConSistency Resolution (FICSR) system.

Semi-supervised model-based document clustering: A comparative study

Semi-supervised learning has become an attractive methodology for improving classification models and is often viewed as using unlabeled data to aid supervised learning. However, it can also be viewed as using labeled data to help clustering, namely, semi-supervised clustering. Viewing semi-supervised learning from a clustering angle is useful in practical situations when the set of labels available in labeled data are not complete, i.e., unlabeled data contain new classes that are not present in labeled data. This paper analyzes several multinomial model-based semi-supervised document clustering methods under a principled model-based clustering framework. The framework naturally leads to a deterministic annealing extension of existing semi-supervised clustering approaches. We compare three (slightly) different semi-supervised approaches for clustering documents: Seeded damnl, Constrained damnl, and Feedback-based damnl, where damnl stands for multinomial model-based deterministic annealing algorithm. The first two are extensions of the seeded k-means and constrained k-means algorithms studied by Basu et al. (2002); the last one is motivated by Cohn et al. (2003). Through empirical experiments on text datasets, we show that: (a) deterministic annealing can often significantly improve the performance of semi-supervised clustering; (b) the constrained approach is the best when available labels are complete whereas the feedback-based approach excels when available labels are incomplete.

Differentiated Real-Time Data Services for E-Commerce Applications

The demand for real-time e-commerce data services has been increasing recently. In many e-commerce applications, it is essential to process user requests within their deadlines, i.e., before the market status changes, using fresh data reflecting the current market status. However, current data services are poor at processing user requests in a timely manner using fresh data. To address this problem, we present a differentiated real-time data service framework for e-commerce applications. User requests are classified into several service classes according to their importance, and they receive differentiated real-time performance guarantees in terms of deadline miss ratio. At the same time, a certain data freshness is guaranteed for all transactions that commit within their deadlines. A feedback-based approach is applied to differentiate the deadline miss ratio among service classes. Admission control and adaptable update schemes are applied to manage potential overload. A simulation study, which reflects the e-commerce data semantics, shows that our approach can achieve a significant performance improvement compared to baseline approaches. Our approach can support the specified per-class deadline miss ratios maintaining the required data freshness even in the presence of unpredictable workloads and data access patterns, whereas baseline approaches fail.

Model Predictive Statistical Process Control of Chemical Plants

Statistical process control aims at improving process operation by distinguishing abnormal process conditions from common cause variations. Improved process operation then results from correcting the abnormal conditions. In this paper an alternative, feedback-based approach to process quality improvement is discussed. The goal of the approach is to use existing process measurements to help reduce the variability of product quality when its online measurement is not feasible. The approach is model based, and it uses principal component analysis to compress the selected process measurements into scores, which are controlled. One or more manipulated setpoints are chosen and varied to counteract the effect of stochastic process disturbances on product quality. The approach discussed assumes that the selected process measurements correlate with product quality and that the stochastic disturbances that cause product variability are stationary. The methodology is illustrated on the Tennessee-Eastman process, where a 44% reduction in product variation is achieved. When nonstationary upsets such as steps occur, the score control setpoints need to be adjusted. A steady-state model predictive controller is discussed which overcomes the problem caused by step upsets. The application presented in the paper involves one manipulated variable, but the methodology is multivariable in nature.

Measurements of human force control during a constrained arm motion using a force-actuated joystick

1. When interacting with the environment, human arm movements may be prevented in certain directions (i.e., when sliding the hand along a surface) resulting in what is called a "constrained motion." In the directions that the movement is restricted, the subject is instead free to control the forces against the constraint. 2. Control strategies for constrained motion may be characterized by two extreme models. Under the active compliance model, an essentially feedback-based approach, measurements of contact force may be used in real time to modify the motor command and precisely control the forces generated against the constraint. Under the passive compliance model the motion would be executed in a feedforward manner, using an internal model of the constraint geometry. The feedforward model relies on the compliant behavior of the passive mechanical system to maintain contact while avoiding excessive contact forces. 3. Subjects performed a task in which they were required to slide the hand along a rigid surface. This task was performed in a virtual force environment in which contact forces were simulated by a two-dimensional force-actuated joystick. Unknown to the subject, the orientation of the surface constraint was varied from trial to trial, and contact force changes induced by these perturbations were measured. 4. Subjects showed variations in contact force correlated with the direction of the orientation perturbation. "Upward" tilts resulted in higher contact forces, whereas "downward" tilts resulted in lower contact forces. This result is consistent with a feedforward-based control of a passively compliant system. 5. Subject responses did not, however, correspond exactly to the predictions of a static analysis of a passive, feedforward-controlled system. A dynamic analysis reveals a much closer resemblance between a passive, feedforward model and the observed data. Numerical simulations demonstrate that a passive, dynamic system model of the movement captures many more of the salient features observed in the measured human data. 6. We conclude that human subjects execute surface-following motions in a largely feedforward manner, using an a priori model of the surface geometry. The evidence does not suggest that active, real time use of force feedback is used to guide the movement or to control limb impedance. We do not exclude, however, the possibility that the internal model of the constraint is updated at somewhat longer latencies on the basis of proprioceptive information.

Occult AIDS.

<h3>Abstract</h3> This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach. We found, as hypothesised, that subject performance was significantly higher on the second task if it shared the same subspace as the first. Additionally, accuracy was positively correlated over subjects learning same-subspace tasks, and negatively correlated for those learning different-subspace tasks. These results were compared to the behaviour of a Neural Network model trained using sequential Bayesian learning. Human performance was found to be consistent with a minimal capacity variant of this model. Networks with an increased representational capacity, and networks without Bayesian learning, did not show these transfer effects. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning. <h3>Author summary</h3> How does knowledge gained from previous experience affect learning of new tasks ? This question of “Transfer Learning” has been addressed by teachers, psychologists, and more recently by researchers in the fields of neural networks and machine learning. Leveraging constructs from machine learning, we designed pairs of learning tasks that either shared or did not share a common subspace. We compared the dynamics of transfer learning in humans with those of a multitask neural network model, finding that human performance was consistent with a minimal capacity variant of the model. Learning was boosted in the second task if the same subspace was shared between tasks. Additionally, accuracy between tasks was positively correlated but only when they shared the same subspace. Our results highlight the roles of subspaces, showing how they could act as a learning boost if shared, and be detrimental if not.